Techniques for measuring the similarity between nodes within a single electronic classification scheme have been developed and a number are well-known in the art. However, attempting to measure the similarity between nodes across different electronic classification schemes, which often address different domains of knowledge and/or contain orthogonal networks of concepts, often produces results that are not easily and/or reasonably integrated. This can be due to the fact that the certain similarity measures are appropriate for some schemes, but not others, and that each similarity measure can express distinct senses of similarity (i.e., intra-scheme and inter-scheme) making results from such similarity measures incomparable. Therefore, a need exists for improved similarity measurement methods and apparatuses, especially across different electronic classification schemes.